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    <h1 class="title">StackExchange.Redis 系列 10：Profiling | 性能分析</h1>
  


      
        <time datetime="2020-01-07T10:06:55.000Z">
          create: 2020-01-07 18:06:55 | update: 2020-01-08 14:15:15
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    <div class="entry">
      
      <blockquote>
<ul>
<li>本系列博文是“伪”官方文档翻译，并非完全将官方文档进行翻译，而是我在查阅、测试原始文档并转换为自己东西后进行的“准”翻译。</li>
<li>原始文档见此：<a href="https://stackexchange.github.io/StackExchange.Redis/" target="_blank" rel="noopener">https://stackexchange.github.io/StackExchange.Redis/</a></li>
<li>本系列本博文基于 redis 5.0.6，系列中部分博文跟官方文档有出入，有不同见解 / 说明不当的地方，还请大家不吝拍砖。</li>
</ul>
</blockquote>
<p>StackExchange.Redis公开了一些方法和类型来启用性能分析。</p>
<p>性能分析接口由以下几个接口和方法组成：</p>
<ul>
<li>IProfiler</li>
<li>ConnectionMultiplexer.RegisterProfiler(IProfiler)</li>
<li>ConnectionMultiplexer.BeginProfiling(object)</li>
<li>ConnectionMultiplexer.FinishProfiling(object),</li>
<li>IProfiledCommand</li>
</ul>
<p>你可以通过 ConnectionMultiplexer 来注册一个 IProfiler 实例，无法被更改。</p>
<a id="more"></a>
<p>您可以通过调用 BeginProfiling(object) 方法对给定的上下文（如线程，Http Request等）进行概要分析，并通过调用 FinishProfiling(object) 方法来完成概要分析。</p>
<p>FinishProfiling(object) 返回 IProfiledCommands 的集合，其中包含所有发送给 Redis 的命令的时间信息。</p>
<ul>
<li>这里的 Context 依赖于特定的应用程序。</li>
<li></li>
</ul>
<p>StackExchange.Redis 目前提供的性能计时器信息包括：</p>
<ul>
<li>Redis 服务器信息。</li>
<li>被查询的 Redis 数据库信息。</li>
<li>执行的 redis 命令。</li>
<li>路由命令的标记信息。</li>
<li>所执行命令的初始化连接所用时间。</li>
<li>命令入队所耗时间。</li>
<li>命令入队之后再发送命令所耗的时间。</li>
<li>从命令发送后，从 redis 服务器接收到响应所耗的时间。</li>
<li>接收到响应后，处理响应所耗的时间。</li>
<li>如果命令是响应集群 ASK 或 MOVED 响应，则获取原始命令是什么。</li>
</ul>
<h1 id="通过-BeginProfiling-object-，FinishProfiling-object-和-IProfiler-GetContext-进行性能分析"><a href="#通过-BeginProfiling-object-，FinishProfiling-object-和-IProfiler-GetContext-进行性能分析" class="headerlink" title="通过 BeginProfiling(object)，FinishProfiling(object) 和 IProfiler.GetContext() 进行性能分析"></a>通过 BeginProfiling(object)，FinishProfiling(object) 和 IProfiler.GetContext() 进行性能分析</h1><p>因为 StackExchange.Redis 采用的是异步接口，因此在性能分析的时候，需要外部协助才能将相关命令组合在一起：这里可以在调用 BeginProfiling(object)，FinishProfiling(object) 和 IProfiler.GetContext() 方法的时候，使用上下文对象来实现。</p>
<h2 id="测试用例1"><a href="#测试用例1" class="headerlink" title="测试用例1"></a>测试用例1</h2><p></p><p class="code-caption" data-lang="csharp" data-line_number="backend" data-trim_indent="backend" data-label_position="outer" data-labels_left="Code" data-labels_right=":" data-labels_copy="Copy Code"><span class="code-caption-label"></span></p><p></p>
<pre><code class="csharp">class ToyProfiler : IProfiler
{
    public ConcurrentDictionary&lt;Thread, object&gt; Contexts = new ConcurrentDictionary&lt;Thread, object&gt;();

    public object GetContext()
    {
        object ctx;
        if(!Contexts.TryGetValue(Thread.CurrentThread, out ctx)) ctx = null;

        return ctx;
    }
}

// ...

ConnectionMultiplexer conn = /* initialization */;
var profiler = new ToyProfiler();
var thisGroupContext = new object();

conn.RegisterProfiler(profiler);

var threads = new List&lt;Thread&gt;();

for (var i = 0; i &lt; 16; i++)
{
    var db = conn.GetDatabase(i);

    var thread =
        new Thread(
            delegate()
            {
                var threadTasks = new List&lt;Task&gt;();

                for (var j = 0; j &lt; 1000; j++)
                {
                    var task = db.StringSetAsync(&quot;&quot; + j, &quot;&quot; + j);
                    threadTasks.Add(task);
                }

                Task.WaitAll(threadTasks.ToArray());
            }
        );

    profiler.Contexts[thread] = thisGroupContext;

    threads.Add(thread);
}

conn.BeginProfiling(thisGroupContext);

threads.ForEach(thread =&gt; thread.Start());
threads.ForEach(thread =&gt; thread.Join());

IEnumerable&lt;IProfiledCommand&gt; timings = conn.FinishProfiling(thisGroupContext);
</code></pre>
<ul>
<li>以上例子将包含 16000 个 IProfiledCommand 对象—每个命令一个。</li>
</ul>
<h2 id="测试用例2"><a href="#测试用例2" class="headerlink" title="测试用例2"></a>测试用例2</h2><p></p><p class="code-caption" data-lang="csharp" data-line_number="backend" data-trim_indent="backend" data-label_position="outer" data-labels_left="Code" data-labels_right=":" data-labels_copy="Copy Code"><span class="code-caption-label"></span></p><p></p>
<pre><code class="csharp">ConnectionMultiplexer conn = /* initialization */;
var profiler = new ToyProfiler();

conn.RegisterProfiler(profiler);

var threads = new List&lt;Thread&gt;();

var perThreadTimings = new ConcurrentDictionary&lt;Thread, List&lt;IProfiledCommand&gt;&gt;();

for (var i = 0; i &lt; 16; i++)
{
    var db = conn.GetDatabase(i);

    var thread =
        new Thread(
            delegate()
            {
                var threadTasks = new List&lt;Task&gt;();

                conn.BeginProfiling(Thread.CurrentThread);

                for (var j = 0; j &lt; 1000; j++)
                {
                    var task = db.StringSetAsync(&quot;&quot; + j, &quot;&quot; + j);
                    threadTasks.Add(task);
                }

                Task.WaitAll(threadTasks.ToArray());

                perThreadTimings[Thread.CurrentThread] = conn.FinishProfiling(Thread.CurrentThread).ToList();
            }
        );

    profiler.Contexts[thread] = thread;

    threads.Add(thread);
}

threads.ForEach(thread =&gt; thread.Start());
threads.ForEach(thread =&gt; thread.Join());
</code></pre>
<ul>
<li>perThreadTimings 最终将带有 16 个条目，每个条目包含 1000 个 IProfilingCommand，由发出它们的线程键入。</li>
</ul>
<h2 id="在-MVC5-中使用性能分析"><a href="#在-MVC5-中使用性能分析" class="headerlink" title="在 MVC5 中使用性能分析"></a>在 MVC5 中使用性能分析</h2><p>自定义一个分析器</p>
<p></p><p class="code-caption" data-lang="csharp" data-line_number="backend" data-trim_indent="backend" data-label_position="outer" data-labels_left="Code" data-labels_right=":" data-labels_copy="Copy Code"><span class="code-caption-label"></span></p><p></p>
<pre><code class="csharp">public class RedisProfiler : IProfiler
{
    const string RequestContextKey = &quot;RequestProfilingContext&quot;;

    public object GetContext()
    {
        var ctx = HttpContext.Current;
        if (ctx == null) return null;

        return ctx.Items[RequestContextKey];
    }

    public object CreateContextForCurrentRequest()
    {
        var ctx = HttpContext.Current;
        if (ctx == null) return null;

        object ret;
        ctx.Items[RequestContextKey] = ret = new object();

        return ret;
    }
}
</code></pre>
<p>在 Global.ascx.cs 中调用 RedisProfiler</p>
<p></p><p class="code-caption" data-lang="csharp" data-line_number="backend" data-trim_indent="backend" data-label_position="outer" data-labels_left="Code" data-labels_right=":" data-labels_copy="Copy Code"><span class="code-caption-label"></span></p><p></p>
<pre><code class="csharp">protected void Application_BeginRequest()
{
    var ctxObj = RedisProfiler.CreateContextForCurrentRequest();
    if (ctxObj != null)
    {
        RedisConnection.BeginProfiling(ctxObj);
    }
}

protected void Application_EndRequest()
{
    var ctxObj = RedisProfiler.GetContext();
    if (ctxObj != null)
    {
        var timings = RedisConnection.FinishProfiling(ctxObj);

        // do what you will with `timings` here
    }
}
</code></pre>
<ul>
<li>以上代码会将所有的 redis 命令（包括同步和异步方法）与初始划它们的 http 请求 进行分组。</li>
</ul>
<h1 id="通过ProfilingSession-ConnectionMultiplexer-RegisterProfiler-Func-ProfilingSession-FinishProfiling-和-IProfiledCommand-进行性能分析"><a href="#通过ProfilingSession-ConnectionMultiplexer-RegisterProfiler-Func-ProfilingSession-FinishProfiling-和-IProfiledCommand-进行性能分析" class="headerlink" title="通过ProfilingSession, ConnectionMultiplexer.RegisterProfiler(Func), ProfilingSession.FinishProfiling() 和 IProfiledCommand 进行性能分析"></a>通过ProfilingSession, ConnectionMultiplexer.RegisterProfiler(Func<profilingsession>), ProfilingSession.FinishProfiling() 和 IProfiledCommand 进行性能分析</profilingsession></h1><p>您需要注册一个回调（Func <profilingsession>），该回调函数为 ProfilingSession 提供了一个 ConnectionMultiplexer 实例。</profilingsession></p>
<p>必要时，StackExchange.Redis 会调用该回调函数，如果返回非空，则会将此操作附加到 session 中。</p>
<p>在特定性能分析会话上调用 FinishProfiling 将会返回一个 IProfiledCommands 的集合，其中包含 ConnectionMultiplexer 发送到 Redis 的所有命令的耗时信息。</p>
<p>目前提供的时间信息见上方（已罗列出）。</p>
<h2 id="例1"><a href="#例1" class="headerlink" title="例1"></a>例1</h2><p></p><p class="code-caption" data-lang="csharp" data-line_number="backend" data-trim_indent="backend" data-label_position="outer" data-labels_left="Code" data-labels_right=":" data-labels_copy="Copy Code"><span class="code-caption-label"></span></p><p></p>
<pre><code class="csharp">class AsyncLocalProfiler
{
    private readonly AsyncLocal&lt;ProfilingSession&gt; perThreadSession = new AsyncLocal&lt;ProfilingSession&gt;();

    public ProfilingSession GetSession()
    {
        var val = perThreadSession.Value;
        if (val == null)
        {
            perThreadSession.Value = val = new ProfilingSession();
        }
        return val;
    }
}
...
var profiler = new AsyncLocalProfiler();
multiplexer.RegisterProfiler(profiler.GetSession);
</code></pre>
<ul>
<li>以上示例会为每个异步上下文自动创建一个性能分析会话（如果上下文拥有会话，则会重用该会话）。</li>
<li>在某些单元测试中，可以使用以下代码来获取执行的操作和耗时：<br><p class="code-caption" data-lang="csharp" data-line_number="backend" data-trim_indent="backend" data-label_position="outer" data-labels_left="Code" data-labels_right=":" data-labels_copy="Copy Code"><span class="code-caption-label"></span></p><pre><code class="csharp">var commands = profiler.GetSession().FinishProfiling();
</code></pre>
</li>
</ul>
<h2 id="例2"><a href="#例2" class="headerlink" title="例2"></a>例2</h2><blockquote>
<p>将不同线程发出的命令关联到一起</p>
</blockquote>
<p></p><p class="code-caption" data-lang="csharp" data-line_number="backend" data-trim_indent="backend" data-label_position="outer" data-labels_left="Code" data-labels_right=":" data-labels_copy="Copy Code"><span class="code-caption-label"></span></p><p></p>
<pre><code class="csharp">class ToyProfiler
{
    // note this won&#39;t work over &quot;await&quot; boundaries; &quot;AsyncLocal&quot; would be necessary there
    private readonly ThreadLocal&lt;ProfilingSession&gt; perThreadSession = new ThreadLocal&lt;ProfilingSession&gt;();
    public ProfilingSession PerThreadSession
    {
        get =&gt; perThreadSession.Value;
        set =&gt; perThreadSession.Value = value;
    }
}

// ...

ConnectionMultiplexer conn = /* initialization */;
var profiler = new ToyProfiler();
var sharedSession = new ProfilingSession();

conn.RegisterProfiler(() =&gt; profiler.PerThreadSession);

var threads = new List&lt;Thread&gt;();

for (var i = 0; i &lt; 16; i++)
{
    var db = conn.GetDatabase(i);

    var thread =
        new Thread(
            delegate()
            {
                // set each thread to share a session
                profiler.PerThreadSession = sharedSession;

                var threadTasks = new List&lt;Task&gt;();

                for (var j = 0; j &lt; 1000; j++)
                {
                    var task = db.StringSetAsync(&quot;&quot; + j, &quot;&quot; + j);
                    threadTasks.Add(task);
                }

                Task.WaitAll(threadTasks.ToArray());
            }
        );

    threads.Add(thread);
}

threads.ForEach(thread =&gt; thread.Start());
threads.ForEach(thread =&gt; thread.Join());

var timings = sharedSession.FinishProfiling();
</code></pre>
<ul>
<li>以上示例最终将包含 16000 个 IProfiledCommand 对象，每个命令一个。</li>
</ul>
<h2 id="例3"><a href="#例3" class="headerlink" title="例3"></a>例3</h2><p></p><p class="code-caption" data-lang="csharp" data-line_number="backend" data-trim_indent="backend" data-label_position="outer" data-labels_left="Code" data-labels_right=":" data-labels_copy="Copy Code"><span class="code-caption-label"></span></p><p></p>
<pre><code class="csharp">ConnectionMultiplexer conn = /* initialization */;
var profiler = new ToyProfiler();

conn.RegisterProfiler(() =&gt; profiler.PerThreadSession);

var threads = new List&lt;Thread&gt;();

var perThreadTimings = new ConcurrentDictionary&lt;Thread, List&lt;IProfiledCommand&gt;&gt;();

for (var i = 0; i &lt; 16; i++)
{
    var db = conn.GetDatabase(i);

    var thread =
        new Thread(
            delegate()
            {
                var threadTasks = new List&lt;Task&gt;();
                profiler.PerThreadSession = new ProfilingSession();

                for (var j = 0; j &lt; 1000; j++)
                {
                    var task = db.StringSetAsync(&quot;&quot; + j, &quot;&quot; + j);
                    threadTasks.Add(task);
                }

                Task.WaitAll(threadTasks.ToArray());

                perThreadTimings[Thread.CurrentThread] = profiler.PerThreadSession.FinishProfiling().ToList();
            }
        );
    threads.Add(thread);
}

threads.ForEach(thread =&gt; thread.Start());
threads.ForEach(thread =&gt; thread.Join());
</code></pre>
<ul>
<li>以上示例中，perThreadTimings 最终将会创建 16 个条目，每个条目 1000 个 IProfilingCommand ，由发出它们的线程键入。</li>
</ul>
<h2 id="在-MVC5-中使用性能分析-1"><a href="#在-MVC5-中使用性能分析-1" class="headerlink" title="在 MVC5 中使用性能分析"></a>在 MVC5 中使用性能分析</h2><p>首先针对 ConnectionMultiplexer 注册一个 IProfiler：</p>
<p></p><p class="code-caption" data-lang="csharp" data-line_number="backend" data-trim_indent="backend" data-label_position="outer" data-labels_left="Code" data-labels_right=":" data-labels_copy="Copy Code"><span class="code-caption-label"></span></p><p></p>
<pre><code class="csharp">public class RedisProfiler
{
    const string RequestContextKey = &quot;RequestProfilingContext&quot;;

    public ProfilingSession GetSession()
    {
        var ctx = HttpContext.Current;
        if (ctx == null) return null;

        return (ProfilingSession)ctx.Items[RequestContextKey];
    }

    public void CreateSessionForCurrentRequest()
    {
        var ctx = HttpContext.Current;
        if (ctx != null)
        {
            ctx.Items[RequestContextKey] = new ProfilingSession();
        }
    }
}
</code></pre>
<p>在 Global.ascx.cs 中调用 RedisProfiler</p>
<p></p><p class="code-caption" data-lang="csharp" data-line_number="backend" data-trim_indent="backend" data-label_position="outer" data-labels_left="Code" data-labels_right=":" data-labels_copy="Copy Code"><span class="code-caption-label"></span></p><p></p>
<pre><code class="csharp">protected void Application_BeginRequest()
{
    //_redisProfiler 是 RedisProfiler 的一个实例
    _redisProfiler.CreateSessionForCurrentRequest();
}

protected void Application_EndRequest()
{
    var session = _redisProfiler.GetSession();
    if (session != null)
    {
        var timings = session.FinishProfiling();

        // do what you will with `timings` here
    }
}
</code></pre>
<ul>
<li>需要确保该连接创建的时候已经注册 Profiler 了，注册方式如下：</li>
</ul>
<p></p><p class="code-caption" data-lang="csharp" data-line_number="backend" data-trim_indent="backend" data-label_position="outer" data-labels_left="Code" data-labels_right=":" data-labels_copy="Copy Code"><span class="code-caption-label"></span></p><p></p>
<pre><code class="csharp">connection.RegisterProfiler(() =&gt; _redisProfiler.GetSession());
</code></pre>
<ul>
<li>以上代码会将所有的 redis 命令（包括同步和异步方法）与初始划它们的 http 请求 进行分组。</li>
</ul>

      

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